"""simple docstring"""


import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Optional[int]:




						'''simple docstring'''



						def get_masked_lm_array(__lowerCAmelCase							):
												lowerCamelCase__   =F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
												lowerCamelCase__   =tf.train.load_variable(a__  ,       a__							)

												if "kernel" in name:
																		lowerCamelCase__   =array.transpose()

												return torch.from_numpy(a__							)

						def get_encoder_array(__lowerCAmelCase							):
												lowerCamelCase__   =F'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
												lowerCamelCase__   =tf.train.load_variable(a__  ,       a__							)

												if "kernel" in name:
																		lowerCamelCase__   =array.transpose()

												return torch.from_numpy(a__							)

						def get_encoder_layer_array(__lowerCAmelCase  ,       __lowerCAmelCase							):
												lowerCamelCase__   =F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
												lowerCamelCase__   =tf.train.load_variable(a__  ,       a__							)

												if "kernel" in name:
																		lowerCamelCase__   =array.transpose()

												return torch.from_numpy(a__							)

						def get_encoder_attention_layer_array(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							):
												lowerCamelCase__   =F'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
												lowerCamelCase__   =tf.train.load_variable(a__  ,       a__							)
												lowerCamelCase__   =array.reshape(a__							)

												if "kernel" in name:
																		lowerCamelCase__   =array.transpose()

												return torch.from_numpy(a__							)

						print(F'''Loading model based on config from {config_path}...'''							)
						lowerCamelCase__   =BertConfig.from_json_file(a__							)
						lowerCamelCase__   =BertForMaskedLM(a__							)

						# Layers
						for layer_index in range(0  ,       config.num_hidden_layers							):
												lowerCamelCase__   =model.bert.encoder.layer[layer_index]

												# Self-attention
												lowerCamelCase__   =layer.attention.self

												lowerCamelCase__   =get_encoder_attention_layer_array(
												    a__  ,       "_query_dense/kernel"  ,       self_attn.query.weight.data.shape							)
												lowerCamelCase__   =get_encoder_attention_layer_array(
												    a__  ,       "_query_dense/bias"  ,       self_attn.query.bias.data.shape							)
												lowerCamelCase__   =get_encoder_attention_layer_array(
												    a__  ,       "_key_dense/kernel"  ,       self_attn.key.weight.data.shape							)
												lowerCamelCase__   =get_encoder_attention_layer_array(
												    a__  ,       "_key_dense/bias"  ,       self_attn.key.bias.data.shape							)
												lowerCamelCase__   =get_encoder_attention_layer_array(
												    a__  ,       "_value_dense/kernel"  ,       self_attn.value.weight.data.shape							)
												lowerCamelCase__   =get_encoder_attention_layer_array(
												    a__  ,       "_value_dense/bias"  ,       self_attn.value.bias.data.shape							)

												# Self-attention Output
												lowerCamelCase__   =layer.attention.output

												lowerCamelCase__   =get_encoder_attention_layer_array(
												    a__  ,       "_output_dense/kernel"  ,       self_output.dense.weight.data.shape							)
												lowerCamelCase__   =get_encoder_attention_layer_array(
												    a__  ,       "_output_dense/bias"  ,       self_output.dense.bias.data.shape							)

												lowerCamelCase__   =get_encoder_layer_array(a__  ,       "_attention_layer_norm/gamma"							)
												lowerCamelCase__   =get_encoder_layer_array(a__  ,       "_attention_layer_norm/beta"							)

												# Intermediate
												lowerCamelCase__   =layer.intermediate

												lowerCamelCase__   =get_encoder_layer_array(a__  ,       "_intermediate_dense/kernel"							)
												lowerCamelCase__   =get_encoder_layer_array(a__  ,       "_intermediate_dense/bias"							)

												# Output
												lowerCamelCase__   =layer.output

												lowerCamelCase__   =get_encoder_layer_array(a__  ,       "_output_dense/kernel"							)
												lowerCamelCase__   =get_encoder_layer_array(a__  ,       "_output_dense/bias"							)

												lowerCamelCase__   =get_encoder_layer_array(a__  ,       "_output_layer_norm/gamma"							)
												lowerCamelCase__   =get_encoder_layer_array(a__  ,       "_output_layer_norm/beta"							)

						# Embeddings
						lowerCamelCase__   =get_encoder_array("_position_embedding_layer/embeddings"							)
						lowerCamelCase__   =get_encoder_array("_type_embedding_layer/embeddings"							)
						lowerCamelCase__   =get_encoder_array("_embedding_norm_layer/gamma"							)
						lowerCamelCase__   =get_encoder_array("_embedding_norm_layer/beta"							)

						# LM Head
						lowerCamelCase__   =model.cls.predictions.transform

						lowerCamelCase__   =get_masked_lm_array("dense/kernel"							)
						lowerCamelCase__   =get_masked_lm_array("dense/bias"							)

						lowerCamelCase__   =get_masked_lm_array("layer_norm/gamma"							)
						lowerCamelCase__   =get_masked_lm_array("layer_norm/beta"							)

						lowerCamelCase__   =get_masked_lm_array("embedding_table"							)

						# Pooling
						lowerCamelCase__   =BertPooler(config=a__							)
						lowerCamelCase__   =get_encoder_array("_pooler_layer/kernel"							)
						lowerCamelCase__   =get_encoder_array("_pooler_layer/bias"							)

						# Export final model
						model.save_pretrained(a__							)

						# Integration test - should load without any errors ;)
						lowerCamelCase__   =BertForMaskedLM.from_pretrained(a__							)
						print(new_model.eval()							)

						print("Model conversion was done sucessfully!"							)


if __name__ == "__main__":
	a				=argparse.ArgumentParser()
	parser.add_argument(
	    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
	)
	parser.add_argument(
	    '--bert_config_file',
	    type=str,
	    required=True,
	    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
	)
	parser.add_argument(
	    '--pytorch_dump_path',
	    type=str,
	    required=True,
	    help='Path to the output PyTorch model.',
	)
	a				=parser.parse_args()
	convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					['''image_processor''', '''tokenizer''']
 __A			       =					'''ViTImageProcessor'''
 __A			       =					('''CLIPTokenizer''', '''CLIPTokenizerFast''')







 def __init__(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]=None							, lowercase_				:   Dict=None							, **lowercase_				:   List[str])						->	str:


   """simple docstring"""







   _UpperCamelCase           =    None
   if "feature_extractor" in kwargs:
     warnings.warn(
         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
         " instead."							, lowercase_							, )
     _UpperCamelCase           =    kwargs.pop("feature_extractor")

   _UpperCamelCase           =    image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
     raise ValueError("You need to specify an `image_processor`.")
   if tokenizer is None:
     raise ValueError("You need to specify a `tokenizer`.")

   super().__init__(lowercase_							, lowercase_)







 def __call__(		self				:   Any							, lowercase_				:   List[str]=None							, lowercase_				:   int=None							, lowercase_				:   Optional[Any]=None							, lowercase_				:   Dict=None							, **lowercase_				:   Dict)						->	int:


   """simple docstring"""







   if text is None and visual_prompt is None and images is None:
     raise ValueError("You have to specify either text, visual prompt or images.")

   if text is not None and visual_prompt is not None:
     raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt.")

   if text is not None:
     _UpperCamelCase           =    self.tokenizer(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if visual_prompt is not None:
     _UpperCamelCase           =    self.image_processor(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if images is not None:
     _UpperCamelCase           =    self.image_processor(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if visual_prompt is not None and images is not None:
     _UpperCamelCase           =    {
         "pixel_values": image_features.pixel_values,
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
   elif text is not None and images is not None:
     _UpperCamelCase           =    image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   elif visual_prompt is not None:
     _UpperCamelCase           =    {
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
   else:
     return BatchEncoding(data=dict(**lowercase_)							, tensor_type=lowercase_)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, *lowercase_				:   List[Any]							, **lowercase_				:   List[Any])						->	int:


   """simple docstring"""







   return self.tokenizer.batch_decode(*lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, *lowercase_				:   Any							, **lowercase_				:   int)						->	Optional[int]:


   """simple docstring"""







   return self.tokenizer.decode(*lowercase_							, **lowercase_)







 @property
 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	str:


   """simple docstring"""







   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							, lowercase_							, )
   return self.image_processor_class







 @property
 def 		__UpperCAmelCase				(		self				:   int)						->	int:


   """simple docstring"""







   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							, lowercase_							, )
   return self.image_processor




